Methods and systems for detecting repetitive synchronized signal events

ABSTRACT

Repetitive synchronized signal events may be detected in received raw signal data that contains a signal. The type of signal, element length (or minimum interval) and/or other characteristics of such repetitive synchronized signal events may also be optionally determined. The disclosed methods and systems may be implemented for processing signals in real time as part of a receiver or transceiver system, or may be implemented by one or more computer processing components that are configured to process stored raw signal data or signal data received from another source, such as across a computer network.

FIELD OF THE INVENTION

The present invention relates generally to signal processing, and moreparticularly to processing raw signal data to detect repetitivesynchronized signal events therein.

BACKGROUND OF THE INVENTION

Signals are often processed to extract certain events from raw receivedsignal data. Extraction of events from raw signal data requires that theevents be detected within the raw signal data. Examples of events thatmay be present in received signals include repetitive synchronizedevents such as phase transitions in a phase-shift key (PSK) signal,frequency slope changes in a frequency modulated continuous wave (FMCW)signal, and frequency transitions in a frequency-shift key (FSK) signal.Such events occur with a frequency or a sub-frequency of a given baud.Other examples of repetitive synchronized events are envelopes of a PSK,FSK or FMCW signal that repeat in a non-random manner with a grouppattern.

One existing real-time continuous wave (CW)/PSK system attempts torecognize element length for a PSK signal by collecting statistics ofthe time intervals between successive spikes from the rectified averagedelta phase derived from the PSK signal. Using this method, the mostoften occurring interval is recognized as the element length. However,this method is adversely affected by unwanted noise spikes that occurin-between good signal spikes, because such noise spikes change the timeinterval evaluation. For FMCW signals, the Kalman filter followed by afrequency peak detection method may be used to detect time intervalsbetween successive frequency slope changes, and as with processing ofPSK signals, the most often occurring time interval is recognized as theelement length. However, this method is also adversely affected byunwanted noise created frequency slope changes that occur between twogood frequency slope changes.

The Radon transform is known for its use in reconstructing images frommedical computer tomography. In this application, the Radon transformdescribes the absorption of X-ray radation as it traverses in a straightline in the human body. A formula for the Radon transform is:

A = ∫_(S)μ(x)x

where A is the relative X-ray transmission, μ is the absorptioncoefficient, and the integral is taken along a straight line s. Byinverting the above integral equation, an image of the absorptioncoefficient μ is constructed.

A “tau-p” transform is a form of the Radon transform used in seismicsignal processing for attenuating straight line events, like theundesirable direct arrival. In a simple form, the Radon transform sumsdata values that lie on a straight line, and events that have thecharacteristics of a straight line can be identified by a large Radonsum. Because most noise is incoherent, it will not line up nicely in astraight line. Therefore the Radon transform is a useful tool inidentifying straight-line coherent noise events like the direct arrivalfrom a noisy background. In the case of seismic processing, the coherentdirect arrival is identified and removed. Alternatively, the Radontransform may be used to identify and then extract lines, edges, curves,textures, or shapes. The Radon transform may be generalized to anintegral along a curve, in which case it will enhance a curved event.The Hough transform is sometimes used where patterns are extracted froman image.

Time-time plots have been used to display climate study data reflectingintensity of a dry season and to display pulsed signal (i.e., radar)information.

SUMMARY OF THE INVENTION

Disclosed herein are methods and systems that may be implemented torecognize or detect a repetitive synchronized event contained in rawsignal data, identify the signal type, and/or to optionally compute oneor more characteristics of a detected repetitive synchronized event(e.g., element length for a PSK signal, element length and/or frequencychange slope for a FMCW signal, etc.). Using the disclosed methods andsystems, reinforceable repetitive synchronous data (e.g., such asrectified average delta data, envelope data, etc.) may be generated fromraw signal data. A time-time plot may be generated from thereinforceable repetitive synchronous data and a Radon transform sumtaken across the time-time plot in a manner that allows data spikes fromrepetitive synchronized events present in the raw signal data tore-enforce one another regardless of noise spikes that may occurelsewhere in the raw signal data, e.g., so as to transform a particularsignal into a form in such a way that it manifests strong recognizablespectral components. In this regard, the disclosed methods and systemsoffer the advantage of processing the raw signal data using a Radontransform sum to re-enforce the presence of repetitive synchronizedevent data so that they occur in a consistent manner along straightlines on the time-time plot. Although random noise may also be prevalentin the raw signal data, these noise do not generally occur in the formof straight lines on the time-time plot and hence do not result in alarge Radon sum. Thus, a given signal (e.g., radio frequency signal suchas RF communication signal, radar signal, optical signal, acousticsignal such as sonar or seismic signal, etc.) present within the rawsignal data may be reduced to data pattern/spikes of high cyclicity thatre-enforce each other when summed appropriately. This re-enforcement ofrepetitive events enables the detection of a much lower SNR signal thanexisting methods, including signals received from weak transmittersand/or at great distances.

Advantageously, the disclosed methods and systems may be employed toprocess a signal in a manner that produces much cleaner repetitive eventpeaks than conventional signal processing methods employing a Fourierspectrum, and to depict a repetitive “group” pattern of a radar orcommunication signal that would not be readily revealed by the Fourierspectrum. Additionally, the disclosed methods and systems may beemployed to yield a more accurate estimate of the element length withoutresorting to higher number of samples.

Advantageously, the disclosed methods and systems may be implemented inone exemplary embodiment to detect the presence of a repetitivesynchronized event (e.g., from a PSK, FSK or FMCW radar or communicationsignal) in a low signal-to-noise (SNR) environment, i.e., where theradar or communication signal has a low SNR. For example, the presenceof a repetitive synchronized event and element length (i.e., which isthe reciprocal of baud) of the repetitive synchronized event may becomputed in one exemplary embodiment from PSK and FMCW signals havingSNR values of less than or equal to about −10 decibels. In anotherexemplary embodiment, PSK signals having SNR values of less than orequal to about −18 db at 32 MHz bandwidth may be detected and therepetitive synchronized event element length of the PSK signalscomputed. This is as compared to typical conventional methodology thatis only capable of computing the element length from PSK and FMCWsignals having a SNR value of not less than 5 db. The disclosed methodsand systems may also be implemented for deriving element length (i.e.,minimum interval) for detection of poly-phase signals

In one respect, disclosed herein is a method of processing signal datathat includes receiving the signal data; obtaining reinforceablerepetitive synchronous data from the signal data; obtaining time-timedata from the reinforceable repetitive synchronous data, the time-timedata including multiple data traces; adding the time-time data in phaseacross the multiple data traces to obtain a sum of the time-time data;and outputting the sum of the time-time data for at least one ofdisplay, storage, or further processing.

In another respect, disclosed herein is a method of detecting repetitivesynchronized signal events that includes detecting the presence of arepetitive synchronized signal event in signal data from an in-phase sumof reinforceable repetitive synchronous data across multiple tracesobtained from the signal data.

In another respect, disclosed herein is a system for processing signaldata, including one or more system components configured to: receive thesignal data; obtain reinforceable repetitive synchronous data from theI-Q data; obtain time-time data from the reinforceable repetitivesynchronous data, the time-time data including multiple data traces; andadd the time-time data in phase across the multiple data traces toobtain a sum of the time-time data.

In another respect, disclosed herein is a system for detectingrepetitive synchronized signal events, the system including: an eventdetector, the event detector being configured to receive signal data andobtain an in-phase sum of reinforceable repetitive synchronous data fromthe signal data. In one embodiment, the event detector may be furtherconfigured to detect the presence of a repetitive synchronized signalevent in the signal data from the in-phase sum of reinforceablerepetitive synchronous data obtained from the signal data.

In another respect, disclosed herein is a system for processing anddisplaying data, including one or more system components configured to:process time-time data to obtain a Radon sum of the time-time data;provide the Radon sum of the time-time data for display (e.g., byoutputting a data signal suitable for generating a graphicalrepresentation of the Radon sum of the time-time data); and graphicallydisplaying the Radon sum of the time-time data using the provided Radonsum of time-time data (e.g., by using an output data signal to generatea graphical representation of the Radon sum of the time-time data by atleast one of a video display or hard copy print-out).

In another respect, disclosed herein is a method of processing anddisplaying data, including: obtaining time-time data; processing thetime-time data to obtain a Radon sum of the time-time data; providingthe Radon sum of the time-time data for display (e.g., by outputting adata signal suitable for generating a graphical representation of theRadon sum of the time-time data); and utilizing the provided Radon sumof time-time data to provide a graphical display of the Radon sum of thetime-time data (e.g., by using an output data signal to generate agraphical representation of the Radon sum of the time-time data by atleast one of a video display or hard copy print-out).

In another respect, disclosed herein is a system for processing anddisplaying data, including one or more system components configured to:process time-time data to obtain an in-phase sum of the time-time data;provide the in-phase sum of the time-time data for display; andgraphically displaying the in-phase sum of the time-time data using theprovided in-phase sum of time-time data.

In another respect, disclosed herein is a method of processing anddisplaying data, including: obtaining time-time data; processing thetime-time data to obtain an in-phase sum of the time-time data;providing the in-phase sum of the time-time data for display; andutilizing the provided in-phase sum of time-time data to provide agraphical display of the in-phase sum of the time-time data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system as it may be implemented accordingto one exemplary embodiment of the disclosed methods and systems.

FIG. 2 is an example plot of unwrapped phase versus sample number for abinary PSK signal according to one exemplary embodiment of the disclosedmethods and systems.

FIG. 3 is a plot of rectified average delta phase versus sample numberfor the binary PSK s ignal of FIG. 2 according to one exemplaryembodiment of the disclosed methods and systems.

FIG. 4A is a time-time plot of data for the binary PSK signal of FIGS. 2and 3 according to one exemplary embodiment of the disclosed methods andsystems.

FIG. 4B is a time-time plot of data for a binary PSK signal according toone exemplary embodiment of the disclosed methods and systems.

FIG. 5 is a Radon sum plot of the time-time plot data of FIG. 4Aaccording to one exemplary embodiment of the disclosed methods andsystems.

FIG. 6 is a time-time plot of data for the binary PSK signal accordingto one exemplary embodiment of the disclosed methods and systems.

FIG. 7 is a Radon sum plot of the time-time plot data of FIG. 6according to one exemplary embodiment of the disclosed methods andsystems.

FIG. 8 illustrates methodology as it may be implemented according to oneexemplary embodiment of the disclosed methods and systems.

FIG. 9 is a plot of an initial portion of combined rectified averagedelta phase versus sample number for multiple dwells according to oneexemplary embodiment of the disclosed methods and systems.

FIG. 10 is a plot of frequency spectrum obtained by FFT processing ofthe combined rectified average delta phase data of FIG. 9 according toone exemplary embodiment of the disclosed methods and systems.

FIG. 11 is a Radon sum plot pertaining to the signal of FIGS. 9 and 10according to one exemplary embodiment of the disclosed methods andsystems.

FIG. 12A is a Radon sum plot according to one exemplary embodiment ofthe disclosed methods and systems.

FIG. 12B is a refined Radon sum plot based on the Radon sum plot of FIG.12A according to one exemplary embodiment of the disclosed methods andsystems.

FIG. 13 shows plots of average delta phase versus sample number, averagedelta of delta phase versus sample number, and rectified average deltaof delta of delta phase versus sample number according to one exemplaryembodiment of the disclosed methods and systems.

FIG. 14 is a time-time plot of rectified average delta frequency slopeaccording to one exemplary embodiment of the disclosed methods andsystems.

FIG. 15 is a Radon sum plot of the time-time plot data of FIG. 14according to one exemplary embodiment of the disclosed methods andsystems.

FIG. 16 is a Radon sum plot according to one exemplary embodiment of thedisclosed methods and systems.

FIG. 17 is a Radon sum plot according to one exemplary embodiment of thedisclosed methods and systems.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Using the disclosed methods and systems, repetitive synchronized signalevents may be detected in received raw signal data, e.g., received rawradio frequency (RF) signal data that contains a radar or communicationsignal such as a PSK signal, FSK signal or FMCW signal. For a givenreceived raw signal data, the type of signal (e.g., PSK, FSK, FMCW),element length (or minimum interval) and/or other characteristics ofsuch repetitive synchronized signal events may also be optionallydetermined. Examples of such repetitive synchronized signal eventsinclude, but are not limited to, phase transitions in a phase-shift key(PSK) signal, frequency slope changes in a frequency modulatedcontinuous wave (FMCW) signal, frequency transitions in afrequency-shift key (FSK) signal, and envelope of signals that repeatwith a fixed periodic pattern. The disclosed methods and systems may beimplemented in any manner and/or system configuration suitable forachieving the repetitive event detection results described elsewhereherein. For example, the disclosed methods and systems may beimplemented for processing signals in real time as part of a receiver ortransceiver system, or may be implemented by one or more computerprocessing components that are configured to process stored raw signaldata or signal data received from another source, such as across acomputer network.

FIG. 1 illustrates one exemplary embodiment of a system 100 as it may beimplemented to receive raw RF signal data and to detect repetitivesynchronized signal events therein in real time. In the illustratedexemplary embodiment a RF signal 103 (e.g., PSK, FSK, FMCW signal) thatcontains repetitive synchronized signal events is receivedsimultaneously with noise and/or other RF signal energy 102 that is notof interest and that together may be characterized as raw signal data.As illustrated in FIG. 1, system 100 includes an antenna 120 that iscoupled to receive and event detector 114 which, in this exemplaryembodiment, includes receive path component/s 110 coupled to eventdetector component/s 112, it being understood that any otherconfiguration of receive and event detector may be employed that issuitable for performing one or more of the event detection tasksdescribed elsewhere herein. It will be understood that although FIG. 1illustrates a system 100 configured to process RF communication signaldata, that other embodiments of the disclosed methods and systems may beimplemented with systems configured to receive and/or process othertypes of systems, e.g., acoustic signals such as sonar or seismicsignals, radar signals, optical signals, etc.

System 100 is illustrated configured as a receive-only system in FIG. 1.However, it will be understood that in other embodiments the disclosedmethods and systems may be alternatively implemented in a systemconfigured as a transceiver. In addition, it is possible that more thanone antenna 120 may be coupled to receive and event detector 114, and/orthat antenna 120 may be a single element antenna or an antenna array. Itwill also be understood that the disclosed methods and systems may beimplemented using any other system configuration that is suitable foroutputting an in-phase sum (e.g., Radon sum) of time-time data for atleast one of display, storage, and/or further processing. For example, asystem may be configured to receive raw signal data and process the sameto obtain the in-phase sum of time-time data, a system may be configuredto obtain the time-time data itself (e.g., from storage, network,another device, etc.) and to process the time-time data to obtain thein-phase sum of time-time data, or a system may be additionally oralternatively configured with one or more display signal generationcomponents to output for display the in-phase sum (e.g., Radon sum) oftime-time data irrespective of the source of this data, etc.

Thus, FIG. 1 is exemplary only and any other configuration and/orcombination of software, firmware, processor/s (e.g. central processingunit/s, microprocessor/s, field programmable gate array/s or other typeof application specific integrated circuit, graphics processing unit/s,etc.) or other hardware (e.g., graphic display hardware including videodisplay devices such as cathode ray tube display, flat panel display,video projector, etc. or hard-copy display devices such as printer,plotter, etc.) that is suitable for displaying a graphicalrepresentation of an in-phase sum (e.g., Radon sum) of time-time data,and/or for performing other processing tasks (e.g., generating thein-phase sum of time-time data from appropriate input data and/or rawsignal data) described herein. In this regard, examples of hostcomputers or other computer elements that may be suitably employed aredescribed and illustrated in U.S. patent application Ser. No. 11/323,835filed Dec. 30, 2005, which is incorporated herein by reference.

Returning to FIG. 1, antenna 120 is coupled to provide raw RF signaldata 108 that contains signal 103 and noise and/or other RF signalenergy 102 to receive path circuitry 110. Receive path circuitry 110 isconfigured to process or condition and digitize the received raw RFsignal data 108 from antenna 120 so as to provide received raw digitizeddata signal 104 to event detector component/s 112 which may beimplemented as part of a digital signal processor or with any othersuitable combination of software, firmware and/or hardware components.Event detector component/s 112 is configured to receive received rawdata signal 104 from receive path circuitry 110 and to process receivedraw data signal 104 so as to detect the presence of one or morerepetitive synchronized signal events therein, identify the type ofsignal containing the detected repetitive synchronized events, and/or tooptionally determine the element length or other characteristics of thedetected repetitive synchronized signal events. In this regard eventdetector 112 may be configured to process received raw data signal 104by performing (not necessarily in the following order) steps of mixing,filtering, decimation, detection of carrier frequency, I-Q conversion,tuning, and the transform techniques described elsewhere herein.

In the illustrated exemplary embodiment of FIG. 1, an output signal 106is produced by event detector 112 that may include one or more of thefollowing information: 1) the class of the signal, (i.e., either PSK,FSK or FMCW); 2) the element length; 3) the frequency of the carrier; 4)for FMCW signals, the frequency deviation of the chirp; and 5) for FMCWsignal, the slopes of the chirp. In one embodiment, the class of thesignal may be determined by the corresponding signal class algorithmthat produces the best result, e.g., after processing the same signaldata with two or more signal class algorithms. What is considered thebest result may be determined by several factors including, but notlimited to, the characteristics of the Radon sum, and the signalstrength at the baud frequency.

In one exemplary embodiment, the time-time plot may also be graphicallydisplayed (e.g., via video display, hard copy print-out, etc.) inreal-time or at a later time, to serve to indicate to an operator thepossible presence of a signal. In this regard, FIG. 1 shows optionalvideo display 150 (e.g., CRT, LCD flat panel display, etc.) that may bepresent for graphically displaying Radon sum of time-time data or othertype sum of time-time data obtained by adding time-time data in phaseacross multiple data traces. In another embodiment, Radon suminformation or other type of sum of time-time data obtained by addingtime-time data in phase across multiple data traces may be stored, e.g.,output to memory for graphical display and/or further processing at alater time. In yet another embodiment, Radon sum information or othertype of sum of time-time data obtained by adding time-time data in phaseacross multiple data traces may be output for further processing, e.g.,output internally within event detector 112 for further processingwithin event detector 112 or output by event detector 112 as a signal toother processing components.

In the practice of the disclosed methods and systems event detectorcomponent/s, such as event detector component/s 112 of FIG. 1A, may beimplemented using any configuration and/or combination of software,firmware, processor/s or other hardware that is suitable for detectingpresence of repetitive synchronized signal events in a signal in amanner as described elsewhere herein. In one embodiment, event detectorcomponent/s 112 of FIG. 1 may be implemented as a digital-signalprocessor (DSP). Alternatively, or in addition to a DSP, event detectorcomponent/s 112 may be implemented using any other type/s of suitablesignal processor/s, graphics processor/s, etc.

Examples of signals that contain repetitive synchronized signal eventsinclude PSK, FSK and FMCW signals. These signals include encoded databits that may be transformed into a series of level changes (or asimilar form of 0's and 1's). For example, FIG. 2 is an example plot ofunwrapped phase versus I-Q samples (256 samples total) obtained frompolar coordinates, arctan(Q/I), for a binary (poly-phase) PSK signal,e.g., as may be received as signal 103 by system of FIG. 1 and isfurther tuned coarsely. It may be seen if the plotted trace of FIG. 2 isrotated to be horizontal, a relative higher phase level represents “1”,whereas a relative lower phase level represents “0”. Although certain ofthe Figures herein illustrate processing of a PSK signal, it will beunderstood that the disclosed methods and systems may be employed toprocess other types of signals that contain repetitive synchronizedsignal events. For example, differentiation of the unwrapped phaseobtained from the I-Q samples for FSK signal data results in a series offrequency level changes. For FMCW signal data, double differentiation ofthe unwrapped phase obtained from the I-Q samples results in a series offrequency slope level changes.

Referring to processing of a PSK signal, the unwrapped phase data ofFIG. 2 may be first differentiated, followed by a DC removal, and thenfollowed by a rectification (i.e., all negative value data spikeschanged to positive value data spikes) to yield the rectified averagedelta phase data plot of FIG. 3, in which the series of data spikes inFIG. 3 each correspond to one of the series of level changes in FIG. 2.In an alternative embodiment, the process of differentiation andunwrapping may be computed almost simultaneously. The average deltaphase d at an instance is the average of m delta phases centered at thatinstance, where m is arbitrary. A delta phase is the difference of thepreceding phase p from the present phase value p. If p(t1) represents aphase value at time t1, and d(t2) represents the average delta phase attime t2, then at t2=0, we have for m=4:

d(0)={p(2)−p(1)+p(1)−p(0)+p(0)−p(1)+p(−1)−p(−2)}/4; ord(0)={p(2)−p(−2)}/4  (Equation 1)

The average delta phase d is a measure of slope and is one way ofmanifesting PSK signal phase transitions that may be employed in thepractice of one exemplary embodiment of the disclosed methods andsystems. However, it will be understood that any other suitablemethodology may be employed to detect PSK signal phase transitions,e.g., using Radon transform methodology as described in U.S. patentapplication Ser. No. 11/323,835 filed Dec. 30, 2005, which isincorporated herein by reference.

For a PSK signal, each of the data spikes of rectified average deltaphase data of FIG. 3 corresponds to a phase change. In the case of a FSKsignal, the unwrapped phase data may be first differentiated to obtain adifferentiated unwrapped phase plot which then itself may bedifferentiated, and then followed by a rectification to yield arectified average delta frequency data plot containing a series of dataspikes that each correspond to a frequency change. In the case of a FMCWsignal, the unwrapped phase data may be first double-differentiated toobtain a double-differentiated unwrapped phase plot which then itselfmay be differentiated, and then followed by a rectification to yield arectified average delta frequency slope data plot containing a series ofdata spikes that each correspond to a frequency slope change. Thegeneral term “rectified average delta” is used herein in reference toone or more of three signal type cases presented herein (i.e., PSK, FSKand FMCW signal processing). In this regard, the general term “rectifiedaverage delta” is used to refer to each of rectified average deltaphase, rectified average delta frequency, and/or rectified average deltafrequency slope.

Still referring to the rectified average delta phase data of FIG. 3, therectified average delta phase data spikes are separated at multiples ofthe element length, which in this exemplary embodiment is equal to 16 asshown. FIG. 4A is a time-time plot of the data of FIG. 3, in which eachtrace has a wrap around length that equals the element length or minimuminterval (i.e., the reciprocal of the baud) of the PSK signal, which is16 for this exemplary embodiment. In this regard, the most bottom traceof FIG. 4A is formed from the first 16 samples (samples 1 to 16) of FIG.3, the second bottom trace is formed from the second 16 samples (samples17 to 32) of FIG. 3, and so on. To form the time-time plot of FIG. 4A,the rectified average delta phase data of FIG. 3 is used to fill a 2-Darray, TT(i,j), to form the time-time plot of FIG. 4A. Thus d(1), . . ., d(n) would fill the first row of TT, and TT(2, 1)=d(n+1), . . . d(2 n)would fill the second row of TT, etc. The value n is an integer andcontrols the instance of wrap around and is also called the wrap aroundlength (WrapLen). This value, n, is approximately equal to, or isapproximately some multiple, k, of, the element length which is definedas the minimum phase transition interval. Ideally, k should be as smallas possible so as to allow more traces being formed and added together,thus enhancing a greater chance of detection. However, when k is a valueother than 1, a group pattern may be revealed.

As will be described further herein, an initial coarse estimate of thewrap around length (e.g., which is equal to 16 for the embodiment ofFIG. 4A) may be obtained in one exemplary embodiment from fast Fouriertransform (FFT) spectrum of the rectified average delta phase data. Fromthis initial coarse estimate, a Radon sum routine may be performed toarrive at a more accurate value of the minimum interval by comparingresults at various slope.

As shown in FIG. 4A, when the wrap around length is exactly equal to theelement length for a particular signal data being processed, the dataspikes line up vertically (i.e., see data spikes A, B, C and D of FIGS.3 and 4). Therefore, when all the traces of FIG. 4A are summed togethervertically along dashed line 408, the result is a large spike at sample12, as shown in FIG. 5. In this regard, FIG. 5 is a Radon sum thatrepresents a substantially vertical sum across the rows of each of theindividual 16-sample columns of a corresponding time-time plot of FIG.4A. The well-defined peak Radon sum peak of FIG. 5 indicates that asignal has been detected, and the element length (inverse of baud) isgiven by the computed trueWrapLen of the plot of FIG. 5. In general, theRadon sum plot depicts much cleaner signals than the rectified averagedelta (compare FIG. 11 to FIG. 9) and may be used in the practice of thedisclosed methods and systems to more readily calculate the elementlength and the time instances where phase changes are likely to occur ina manner. Advantageously the disclosed methodology is robust enough sothat even if the phases are unwrapped in the wrong direction (in thephase transition region), the final results are unaffected. It should benoted that FIG. 5 represents a Radon sum taken from a time-time plot ofdata that includes an entire set of 3000 samples of rectified averagedelta phase, whereas FIGS. 2 through 4A illustrate data from the first256 samples for ease of illustration.

The time-time plot, besides being elucidative in explaining thecomputation of a maximum sum, may also be used as a distinctive displaycapable of indicating the presence of a signal to the human eye.Computationally, the Radon sum may be obtained by adding successive dataspikes spaced at a distance D apart, where D may be varied (over arange.) Varying D is equivalent to varying the slope of the line alongwhich the Radon sum is computed. In FIG. 5, the trueWrapLen is thedistance D when the best Radon sum is obtained. Thus, in FIG. 5, thetrueWrapLen is a refinement of the initial coarse estimate of theelement length. The Radon sum computation for a particular exemplary Dis depicted in the Matlab codes below:

% inputSignal = rectified average delta % lenData = length ofinputSignal % n = wrap around length and is the nearest integer to theinitial element length estimate % shiftPR = shift per row, +ve towardsright. Usually fractional value. function radonSum = quickRadon(inputSignal,lenData,n,shiftPR ) radonSum = zeros( n,1 ); D = n +shiftPR; for ix=1:n  offset = ix;  k = ix;  while k<=lenData   radonSum(ix ) = radonSum( ix ) + inputSignal( k );   offset = offset + D;   k =floor( offset );  end end

Notice that even if offset is usually a non-integer, it is not necessaryto use interpolated values, i.e., the rectified average delta may not beinterpolated in one exemplary embodiment, although interpolation may beused in another embodiment as described in U.S. patent application Ser.No. 11/323,835 filed Dec. 30, 2005, which is incorporated herein byreference. This is to allow speed of computation and has been found towork satisfactorily. When shiftPR=0, this is equivalent to summingvertically on the time-time plot.

If the inputSignal time series had been rotated right (towardsincreasing time) by s samples, where the final s samples is rotated intothe initial s samples, the new Radon sum time series would likewise havebeen rotated right by an amount equal to s samples, where the original(unshifted) final segment of s samples would be rotated into the initialsegment of s samples. This understanding is helpful during searching fora more exact location of Radon sum peak by interpolation when the peakoccurs at the vicinity of the start or end of the Radon sum time series.In which case, interpolation may wrap around the time series ifnecessary.

The Tau-p transform is a form of Radon transform and is used totransform a 2-D seismic image to a 2-D Tau-p section, where p stands forinverse of slope. The Tau-p transform may also be applied to thetime-time plot to obtain the corresponding Tau-p section where the Radonsum series would be equivalent to a sum of all values repeated atintervals tau=trueWrapLen and at the best p. The Tau-p transform isdiscussed in R. H. Tatham, Multidimensional Filtering of Seismic Data,Proc. IEEE, 1984, pp. 1357-1369, which is incorporated herein byreference.

Another distinct but suitable approach that may be employed in oneexemplary embodiment is the Hough transform which maps the contribution(according to slope) of each point in the time-time plot to the Radonsum series. In this regard, the Hough transform is discussed in M. vanGinkel, C. L. Luengo Hendriks, and L. J. van Vliet, A Short Introductionto the Radon and Hough Transforms and How They Relate to Each Other,Technical Report QI-2004-01, Quantitative Imaging Group, DelftUniversity of Technology, February 2004, 1-11, which is incorporatedherein by reference.

It will be understood that as long as the element length is an integerand the wrap-around length, n, is selected to be equal to the elementlength, or to be a multiple of the element length (e.g., two times theelement length), data spikes of a high amplitude event will line upvertically in a time-time plot. Even in those cases where n deviatessomewhat/fractionally from a multiple of the element length, data spikesof a high amplitude event will line up in a sloped manner in a time-timeplot, as indicated by dashed line 410 for the exemplary time-time plotof FIG. 4B. In such a case, a Radon transform may be used to sum all thetraces of FIG. 4B together along the sloped line of data spikesindicated by dashed line 410 to result in a large spike similar to thatshown in FIG. 5. Therefore, using the disclosed methods and systems,high amplitude events in the time-time plot may be processed to line upin a straight line on a time-time plot, albeit a vertical or slopedline, so that the disclosed methodology may be successfully appliedusing a Radon transform operation even without an accurate initialestimate of the element length, e.g., when there is an inaccuracy inestimating the exact element length.

With regard to the exemplary embodiments of FIGS. 4A and 4B, noise israndom in nature and will not tend to line up in a straight line in atime-time plot whereas the high amplitude events of phase changes (e.g.,of a PSK signal) tend to form a straight line. These high amplitudeevents sum to a relatively large value in the Radon sum of FIG. 4A alonglines parallel to the vertical. In FIG. 4B, the Radon sums occuroptimally along lines having a slope relative to the vertical. For theexemplary embodiments of FIGS. 4A and 4B, Radon summing continues as theline wraps around from the right side to the left side until the top ofthe respective time-time plot is reached. The optimal slope to sum theevent may be computed, for example, by iterating through a range ofslopes. Further processing, such as using the steepest descend method,may be used to accelerate the search process.

As previously described, high amplitude events in a time-time plot of asignal may sometimes lie along a straight line with a slope, meaningthat the line of high amplitude events may not always be vertical. Insuch a non-vertical case, a Radon sum may be taken along the appropriateslope to reveal repetitive synchronized signal events of the signal.FIG. 6 is a time-time plot of data from a PSK signal of anotherexemplary embodiment in which high amplitude events lie along a straightline having a slope. In the embodiment of FIG. 6, the minimum intervalis computed to be 15.7 and is not equal to an integer number, meaningthat the line of high amplitude events is not vertical. In such anembodiment, a Radon sum along an appropriate slope of FIG. 6 reveals thesignal, as shown by the Radon sum plot of FIG. 7, which is taken fromthe data of FIG. 6.

Within a dwell, depending on the minimum interval, there may be manydata spikes pertaining to the event changes (or phase transitions in aPSK signal). In one embodiment of the practice of the disclosed methodsand systems, these data spikes may be appropriately added in-phase by aRadon sum technique yielding a re-enforced Radon sum peak. These dataspikes may be added in-phase because they occur at instances that aremultiples of the minimum interval, and because the data spikes areprocessed so that they are always positive. In this exemplaryembodiment, re-enforcement of the signal is possible, even if the databits are random, because the rectified average delta is used rather thanthe non-rectified average delta or the unwrapped phase which may resultin cancellation of good signals instead of desired re-enforcement. Inthe practice of this embodiment, a peak occurs in the correspondingRadon sum plot when the sum is taken on consecutive elements that arespaced a minimum interval apart. In one exemplary embodiment, animprovement in SNR of √N₁ may be realized, where N₁ is the number of thedata spikes due to phase transitions. Even though the raw signal datamay be noisy and contain false spikes, these spikes, being random innature, do not line up to result in a re-enforced Radon sum peak.

In one exemplary embodiment, multiple dwells of the rectified averagedelta may be added in-phase to result in a further re-enforcement ofrelevant signals. This is possible, for example, when the start time ofeach dwell is available (e.g., available from the dwell header andprovided by the timing circuit of the receiver which may be configuredto log the start time at the start of sampling), allowing adetermination of how much to shift one dwell with respect to anotherbefore adding. In-phased addition of multiple dwells of rectifiedaverage delta data results in an improvement in SNR of √N₂, where N₂ isthe number of dwells, resulting in a total improvement to SNR that is√(N₁N₂). As an example, consider a total 3000 sample data set for abinary PSK signal with a minimum interval of 2 μs, and assuming (theoptimum case of) a fixed data pattern of alternate 0's and 1's. Such acase results in a spike in the rectified average delta every 16 samples(after decimation and filtering). This translates to a value ofN₁=3000/16=187, and a value of N₂=4 dwells that are summed forcomputation of the Radon sum to derive the minimum interval. In thiscase, √(N₁N₂)=27.3, which is 14.4 db of improvement in SNR.

In one exemplary embodiment, the following equations represent thein-phase addition together of multiple dwells of rectified average deltadata to result in a further re-enforcement of relevant signals when atime delay (a) that is less than N*Δt; where N is the cyclical FFT size(number of samples used in the FFT), and Δt is the sampling timeinterval:

Let: d₁(t)=rectified average delta of dwell 1=f(t); and

-   -   d₂(t)=rectified average delta of dwell 2=f(t+a) (time lag of a)

Then: D₁(w)=FT(d₁)=F(w);

-   -   D₂(w)=FT(d₂)=e^(iwa)F(w); and    -   F(w)=e^(−iwa)D₂(w)

And the information in dwell I and dwell 2 should add in phase via thefollowing sum:

D ₁(w)+e ^(−iwa) D ₂(w)=s

As described elsewhere herein, the rectified average delta from signaldata are summed. Rectified average delta transforms abrupt phase changeevents into positive spikes for a PSK signal. For a PSK signal with agiven baud, phase transitions may seem to occur at random if the databits are random. However whenever phase transitions occur, they are alsoexpected to occur at discrete instances of time which are in multiplesof the element length. Therefore, the disclosed methods and systems maybe implemented in one exemplary embodiment so that an in phase sum ofseveral dwells of rectified average delta produces a series ofre-enforced spikes at repetitive instances of the element length. Therectified average delta is an example of the more general class ofreinforceable repetitive synchronous data, ΔΦ. As will be describedfurther herein, other examples of reinforceable repetitive synchronousdata include, but are not limited to, envelope data.

FIG. 8 illustrates methodology 800 that may be implemented according toone exemplary embodiment of the disclosed methods and systems to detecta repetitive synchronized signal event and to optionally determineelement length of a signal, e.g., PSK, FSK or FMCW signal. In oneembodiment, methodology 800 may be performed in real-time to processreceived raw signal data (e.g., received raw data signal 104) and may berepeated in real time as often as needed or desired to meet therequirements of a given signal processing application. Exemplary stepsfor processing of I-Q samples in step 806 to obtain the desiredtransform reinforceable repetitive synchronous data, ΔΦ, may becharacterized as follows for each of PSK, FSK and FMCW signals.

For a PSK signal, rawPhase=arctan(Q/I), andunwrappedPhase=unwrap(rawPhase). The unwrappedPhase is differentiated(or deltas are taken), DC component is removed, and negative spikesreversed to positive, which results in the desired transform ΔΦ (i.e.,rectified average delta phase).

For a FSK signal, theta=arctan(Q/I), unwrappedTheta=unwrap(theta), andunwrappedTheta is then differentiated (or deltas are taken) to givefreqLevels. The freqLevels is differentiated (or deltas are taken), andnegative spikes reversed to positive, which results in the desiredtransform ΔΦ (i.e., rectified average delta frequency). Thedifferentiation of the differentiated unwrapped phase (freqLevels) forFSK may alternatively be replaced with a level change detection routine,which takes the sum of k preceding samples and subtract it from the sumof k subsequent samples. The distance between the group of k precedingsamples and the group of k subsequent samples may also vary. The resultof the level change routine may then be scaled and may also be rectifiedto yield the rectified average delta frequency.

Alternatively, for a FSK signal, the I and Q time series may bedifferentiated to yield the corresponding dI and dQ. From these, theenvelope=+√(dI²+dQ²) may be derived, where the envelope is the frequencytime series consisting of the frequency levels of the FSK signal plus aDC component. A differentiation or a level change detection routine maybe applied to this envelope and followed by a rectification to yield ΔΦ.This alternative FSK signal method is advantageous in that it eliminatesthe need to compute arctan(Q/I), which is more computationally intensiveand tends to introduce additional noise although it may be employed inother embodiments.

For a FMCW signal, theta=arctan(Q/I), unwrappedTheta=unwrap(theta), andunwrappedTheta is then differentiated (or deltas are taken) to givefreq, which is then differentiated (or deltas are taken on freq) to givefreqSlopes. The freqSlopes is differentiated (or deltas are taken), andnegative spikes reversed to positive, which results in the desiredtransform ΔΦ (i.e., rectified average delta frequency slope).Alternatively, the differentiated unwrapped phase (freq) for FMCW may beprocessed by a frequency peak detection routine which transformsfrequency peaks into positive data spikes to yield the rectified averagedelta frequency slope, except that in this case (if the trough infrequency is ignored) the data spike only corresponds to a change frompositive frequency slope to negative frequency slope but not thereverse. Such a frequency peak detection routine looks for localizedmaximum in the frequency time series (the differentiated unwrappedphase) and places a positive data spike corresponding to the location ofmaximum, and a value of zero elsewhere. For this method, the computedtrueWrapLen corresponds to the period that is a sum of the rise time(rising frequency segment) and the fall time (falling frequencysegment). With the former method, the computed trueWrapLen correspondsto the shorter of the rise time or fall time. With the former method, ifalternate spikes (or spikes of the same sign before rectification) areused only, the computed trueWrapLen also corresponds to the sum of therise time and fall time. Therefore, in both cases, the element length ofFMCW, which is defined here as the sum of the rise and fall time, may bededuced.

Alternatively, for a FMCW signal, Kalman filter may be applied directlyto the I-Q samples to obtain the frequency. The frequency peak detectionroutine may then be applied to give ΔΦ. In another alternative for aFMCW signal, an envelope may be computed in a similar manner to the FSKsignal. This envelope gives the frequency time series plus a DC offset.This frequency time series may then be input into the frequency peakdetection routine to yield ΔΦ. Again, this method may be advantageouslyimplemented without the arctan(Q/I) computation, which is also embeddedin the above Kalman filter method.

In contrast to data communication signal, the envelope of a radar signalused for detection may not always correspond to random data bits and mayinstead include a repeatable group pattern. Where no random patterns areinvolved, the envelope may be used in place of the rectified averagedelta as an input to the time-time plot Radon transform method tocompute for the group element length and minimum interval. For FMCW andFSK signals, the envelope are as derived above. For PSK, the envelope isthe unwrapped phase of a finely tuned PSK signal or is the unwrappedphase of a coarsely tuned PSK signal with the average slope removed(e.g, such that the signal in FIG. 2 is rotated to the horizontal).These envelopes are yet other examples of reinforceable repetitivesynchronous data, ΔΦ. In such an embodiment, the Radon sums of theseenvelopes result in higher SNR envelopes from which conventionaltechniques may be applied to extract one or more characteristics of thesignals. For example, FIG. 17 shows a Scout FMCW signal at SNR=−10 db at32 MHz bandwidth, where the Radon sum is obtained from the envelope(which consists of 52436 samples) and depicts a group pattern. From theRadon sum of FIG. 17, conventional techniques such as Kalman filterfollowed by a frequency peak detection routine may be applied to extractthe minimum interval, and the frequency rise and fall slopes.

Still referring to step 806 of FIG. 8, it will be understood thatseveral dwells of ΔΦ may be summed in phase at this time. In thisregard, FIG. 9 shows a coherent (in-phase) sum of 400 dwells ofrectified average delta phase for an exemplary simulated case involvinga PSK signal having a SNR of −18 db at 32 MHz bandwidth. FIG. 9 showsthe first 100 of a total of 3000 samples (spanning a time of 12.5 μsbeing shown).

Next, FFT processing is applied in step 808 to ΔΦ of step 806 andamplitude spectrum data is obtained from the FFT results. FIG. 10 showsamplitude spectrum data (for the 3000 total samples) obtained by FFTprocessing of the rectified average delta for the exemplary data of FIG.9. In step 810, an initial estimate of the element length is obtainedfrom the amplitude spectrum data of step 808. As shown in FIG. 10, thehighest energy spike nearest to (but not at) the zero frequency samplemay be used to give an initial estimate of the element length, 1/f, ofthe PSK signal, where f is the frequency of the highest energy spikenearest to but not at zero frequency. A more exact f may be determinedby interpolation as follows: 1) A curve is formed from the energy(amplitude square) values across three consecutive bins, where the midbin contains the highest energy spike. 2) The frequency value at whichthe energy value of the interpolated curve is maximum gives the moreexact f.

For the exemplary data of FIG. 10, the initial estimate of elementlength may be determined to be 15.999268. From the initial estimate ofthe element length obtained in step 810, a time-time-plot-Radontransform of FIG. 11 is derived in step 812, and then a refined revisedestimate of the element length obtained in step 814 from the data ofFIG. 11. In this regard, presence of a single definitive peak obtainedin the time-time plot Radon sum of step 812 is sufficient to confirm thepresence of a signal and to confirm the estimate of element length. FIG.11 illustrates just such a case, where the element length is confirmedto be 16 samples. As desired or necessary, steps 812 to 814 may beiteratively performed as shown by arrow 816 until a sufficientlydefinitive Radon sum peak is obtained in step 814 to confirm thepresence of a signal and to confirm the estimate of element length.

Still referring to FIG. 10, the iteration 816 may also be performed overseveral amplitude spectrum peaks resulting in several Radon sum series.The best Radon sum series may be selected based on factors such as thenumber of peaks, the well-defined nature of the peaks, etc. Ideally, aRadon sum series results that has only one peak gives the true elementlength (by its trueWrapLen). However, if multiple well-defined Radon sumpeaks are obtained the Radon sum series may reveal a group pattern, andthe element length is the distance between the two peaks that are spacedclosest to one another. Results of several Radon sum series may also becompared to confirm the correctness of conclusion.

It will be understood that the order and methodology of the steps ofFIG. 8 is exemplary only and that any other combination of additional,fewer and/or alternative steps may be employed using the Radon sum of atime-time plot (or other suitable type of in-phase sum of time-timedata) that is suitable for recognizing or detecting a repetitivesynchronized event contained in raw signal data, identifying the signaltype/s, and/or computing element length of a detected repetitivesynchronized event. Furthermore, it will be understood that anycombination of additional, fewer and/or alternative steps may beemployed to provide or otherwise generate an in-phase sum (e.g., Radonsum) of time-time data for at least one of display, storage, or furtherprocessing, regardless of the source of this data (i.e., received signaldata or not). More information on Radon sum processing may be found, forexample, in M. van Ginkel, C. L. Luengo Hendriks, and L. J. van Vliet, AShort Introduction to the Radon and Hough Transforms and How They Relateto Each Other, Technical Report QI-2004-01, Quantitative Imaging Group,Delft University of Technology, February 2004, 1-11, which isincorporated herein by reference. Further, it will be understood thatthe methodology of step 812 is exemplary only, and that any othertechniques other than Radon sum methodology may be employed that issuitable for adding time-time data in phase across multiple data tracesto obtain a sum of time-time data.

As disclosed herein, Radon sum of time-time plots of rectified averagedelta data may be utilized to identify the initial time instances fromwhence subsequent event changes (e.g., phase transitions, frequencyslope changes) may be located or approximately located. In one exemplaryembodiment, this may be employed to enable focusing of re-computationaround narrower regions of the data using different methods. Forexample, the methodology of U.S. patent application Ser. No. 11/323,835filed Dec. 30, 2005 (which has been incorporated herein by reference)may be employed to further reduce or eliminate potential for falserecognition of event changes, or to identify a more exact location ofthe event changes, or to identify other events in the signal data.Furthermore, in another exemplary embodiment, methodology of steps 806through 812 may be performed to “screen” raw signal data for presence ofone or more particular types of signal (e.g., PSK signal, FSK signal,FMCW signal, etc.), for example, prior to further appropriate processingof the identified particular type of signal. For example, the processingperformed in step 806 may be performed to process I-Q samples in amanner that is targeted to identifying the presence of one particulartype of signal of interest (e.g., PSK signal, FSK signal, FMCW signal,etc.), or may be performed using successive processing steps orsimultaneous processing steps each targeted to identifying the presenceof a particular different type of signal of interest. In either case,detection of a repetitive synchronous signal event during the signalprocessing that is targeted to a particular signal of interest indicatesthe presence of that particular type of signal in the raw signal data.

FIG. 12A illustrates another example of time-time-plot-Radon transformobtained in step 812 using an initial estimate of element length (i.e.,32.13) from the strongest interpolated peak of amplitude spectrum dataobtained in step 810 from FFT processing of a different PSK signal fromFIGS. 10 and 11. FIG. 12A shows the trueWrapLen of 32 which may be takenas the more likely value of the (group) element length and illustratesthe better accuracy achieved by the Radon transform compared to thatobtained from the FFT amplitude spectrum (especially when the spectrumconsists of a small number of samples). As shown in FIG. 12A, multiplepeaks (two peaks in this case) are present as a repeatable group patternin the time-time-plot-Radon transform, indicating that the actualelement length is smaller than the initial estimate of step 810 ratherthan the larger group interval estimate derived from FFT spectrum. Insuch a case, the distance between two closest peaks of the repeatablegroup pattern may be used in step 814 to determine a refined estimate ofelement length. However, a more exact location of a Radon sum peak maybe found by interpolation as follows: 1) A curve is formed from theRadon sum values across three consecutive bins, where the mid bincontains peak Radon sum. 2) The sample value at which the Radon sum ofthe interpolated curve is maximum gives the more exact location of theRadon sum peak. When the peak is located near the start or end of theRadon sum time series, the three consecutive bin may wrap around thetime series appropriately as discussed previously.

In the example of FIG. 12A, the distance between the two closest peaksis 8 samples. This distance may then be taken as an initial guess ofelement length and input into the Radon transform routine again, whichproduces FIG. 12B. FIG. 12B indicates that the refined element length isindeed 8 samples because there is one well-defined peak. Thus, detailsof a larger repetitive pattern (spanning several element lengths) may berevealed by a time-time-plot-Radon-transform using ΔΦ, even when detailsof the pattern are not readily interpreted using the FFT spectrum alone.A comparison of FIG. 10 to FIG. 11 shows that thetime-time-plot-Radon-transform of FIG. 11 yields a much cleaner visualpresentation of the presence of element length than the FFT amplitudespectrum plot of FIG. 10. In this regard, FIG. 10 appears noisy incomparison to FIG. 11 which clearly shows the presence of the elementlength. Moreover, a time-time-plot-Radon-transform may be obtained inone exemplary embodiment in a manner that is computationally very fast,and without the need to interpolate the ΔΦ values. When element lengthdeviates from an integral number of samples, a slope in the highamplitude events is yielded in a time-time plot. However, the presenceof this slope may be readily revealed by a time-time-plot Radontransform in a manner that relates very accurately to the deviation fromthe integral number.

In frequency domain, the time shift corresponds to a phase shift. Incertain embodiments it may be also desirable to work with the frequencyspectrum, e.g., to add the frequency spectrums of several dwells. Thisprocess may be become complicated in some cases if a successive dwell isdelayed too long, e.g., by a time delay (a) that is greater than N*Δt,where N is the FFT length, and Δt is the sampling interval. However, inembodiments where it is possible to acquire a long continuous dwell, alarge value of N₁ (i.e., number of phase transition data spikes) may bedetermined. This large number of samples may be used to recognize ordetect a repetitive synchronized event contained in raw signal dataunder low SNR conditions.

FIGS. 13 through 15 illustrate one exemplary embodiment in which thedisclosed methods and systems may be utilized to detect changes in asignal with a given baud, in this case a triangular FMCW signal having aperiod of 30 μsec with a SNR of 5 at a bandwidth of 32 MHz. As firstshown in FIG. 13, the average of the delta changes are computed threetimes (i.e., in three stages) starting from the unwrapped phase of theI-Q samples. In FIG. 13, the upper time series plot is the equivalent offrequency versus time, the middle time series plot is the equivalent offrequency slope versus time, and the bottom time series plot representsfrequency slope changes versus time. Appropriate filters (e.g., low-passfilter to reduce differentiation noise) may also be applied at eachstage to smooth the time series. In this exemplary embodiment, the final(bottom) time series plot of FIG. 13 is used to compute the time-timeplot of FIG. 14. FIG. 15 shows the Radon sum result from summing thetime-time plot of FIG. 14 (along lines parallel to the vertical), andappears much cleaner than the final (bottom plot) of FIG. 13. The Radonsum peaks of FIG. 15 give the instances where the initial frequencyslope changes occur in the FMCW signal, and from the intervals betweenthe two peaks, the rise and fall time may be deduced (the minimuminterval is the sum of rise and fall time). Thus, it may be seen thatthe disclosed time-time plot Radon transform methodology is applicableto detection of repetitive synchronized signal events in any signal witha given baud from which a measure of event changes may be computed,e.g., FSK signals, polyphase signals, etc.

FIG. 16 illustrates one exemplary embodiment in which a dwell contains arepeatable “group” pattern, which is revealed in the Radon sum oftime-time plot as shown. In this exemplary embodiment, the group consistof “level” intervals of 3T, T and 2T, which are repeated consecutivelyin the dwell. In this embodiment, T may be recognized as the minimuminterval from the data of FIG. 16. The SNR of the signal data for thisembodiment is −18 db at 32 MHz bandwidth. At this low SNR, it would bevirtually impossible to determine each phase transition by individualanalysis, however, the disclosed methods and systems may beadvantageously implemented to leverage in phase summation of 400 dwellsto reveal the group pattern as shown in FIG. 16.

As disclosed herein, Radon sum of time-time plots of rectified averagedelta data ΔΦ, may be utilized to identify the initial time instancesfrom whence subsequent event changes (e.g., phase transitions, frequencyslope changes) may be located or approximately located. In one exemplaryembodiment, this may be employed to enable focusing of re-computationaround narrower regions of the data using different methods. Forexample, the methodology of U.S. patent application Ser. No. 11/323,835filed Dec. 30, 2005 (which has been incorporated herein by reference)may be employed to further reduce or eliminate potential for falserecognition of event changes, or to identify a more exact location ofthe event changes, or to identify other events in the signal data.

While the invention may be adaptable to various modifications andalternative forms, specific embodiments have been shown by way ofexample and described herein. However, it should be understood that theinvention is not intended to be limited to the particular formsdisclosed. Rather, the invention is to cover all modifications,equivalents, and alternatives falling within the spirit and scope of theinvention as defined by the appended claims. Moreover, the differentaspects of the disclosed methods and systems may be utilized in variouscombinations and/or independently. Thus the invention is not limited toonly those combinations shown herein, but rather may include othercombinations.

1. A method of processing signal data, comprising: receiving said signaldata; obtaining reinforceable repetitive synchronous data from saidsignal data; obtaining time-time data from said reinforceable repetitivesynchronous data, said time-time data comprising multiple data traces;adding said time-time data in phase across said multiple data traces toobtain a sum of said time-time data; and outputting said sum of saidtime-time data for at least one of display, storage, or furtherprocessing.
 2. The method of claim 1, wherein said reinforceablerepetitive synchronous data comprises rectified average delta data. 3.The method of claim 1, further comprising identifying a high amplitudeevent in said sum of said time-time data, and determining the presenceof a repetitive synchronous event in said signal data from said highamplitude event.
 4. The method of claim 1, further comprisingidentifying a pattern within said sum of said time-time data, anddetermining one or more characteristics of said signal data.
 5. Themethod of claim 1, further comprising providing an initial estimate ofelement length of a repetitive synchronous event in said signal data;then orienting said multiple data traces of said time-time data relativeto each other based on said estimated element length prior to addingsaid time-time data in phase across said multiple data traces; and thendetermining a revised estimate of said element length based on said sumof said time-time data.
 6. The method of claim 5, further comprisingobtaining an amplitude spectrum of said reinforceable repetitivesynchronous data; and then providing said initial estimate of elementlength of said repetitive synchronous event from said amplitude spectrumof said reinforceable repetitive synchronous data.
 7. The method ofclaim 6, further comprising applying a Fast Fourier Transform to saidreinforceable repetitive synchronous data to obtain said amplitudespectrum; and adding said time-time data in phase across said multipledata traces to obtain a Radon sum of said time-time data.
 8. The methodof claim 1, said method comprising: receiving a dwell of said signaldata; obtaining reinforceable repetitive synchronous data from said I-Qdata of said dwell of said signal data; obtaining time-time data fromsaid reinforceable repetitive synchronous data corresponding to saiddwell of said signal data, said time-time data comprising multiple datatraces; and adding said time-time data in phase across said multipledata traces to obtain a sum of said time-time data corresponding to saiddwell of said signal data.
 9. The method of claim 1, said methodcomprising: receiving multiple dwells of said signal data; obtainingreinforceable repetitive synchronous data from said I-Q data of each ofsaid multiple dwells of said signal data; adding said reinforceablerepetitive synchronous data corresponding to said multiple dwells ofsaid signal data to obtain a sum of reinforceable repetitive synchronousdata; obtaining time-time data from said sum of reinforceable repetitivesynchronous data, said time-time data comprising multiple data traces;and adding said time-time data in phase from said sum of reinforceablerepetitive synchronous data across said multiple data traces to obtain asum of said time-time data corresponding to said multiple dwells of saidsignal data.
 10. A method of detecting repetitive synchronized signalevents, said method comprising detecting the presence of a repetitivesynchronized signal event in signal data from an in-phase sum ofreinforceable repetitive synchronous data across multiple tracesobtained from said signal data.
 11. The method of claim 10, wherein saidreinforceable repetitive synchronous data comprises rectified averagedelta data.
 12. The method of claim 10, wherein said signal datacomprises raw signal data that includes at least one signal, saidrepetitive synchronized signal event corresponding to said at least onesignal.
 13. The method of claim 12, wherein said signal comprises aphase-shift key (PSK) signal and said repetitive synchronized signalevent comprises a phase transition in said PSK signal; or wherein saidsignal comprises a frequency modulated continuous wave (FMCW) signal andsaid repetitive synchronized signal event comprises a frequency slopechange in said FMCW signal; or wherein said signal comprises afrequency-shift key (FSK) signal and said repetitive synchronized signalevent comprises a frequency transition in said FSK signal.
 14. Themethod of claim 12, wherein said repetitive synchronized signal eventcomprises an envelope in the said signal encoding a fixed periodic datapattern.
 15. The method of claim 12, wherein said signal comprises aradar signal.
 16. The method of claim 12, further comprising determiningthe signal type of said signal based on said in-phase sum ofreinforceable repetitive synchronous data obtained from said signaldata.
 17. The method of claim 16, wherein said signal comprises a PSKsignal and wherein said in-phase sum of reinforceable repetitivesynchronous data is obtained from delta phase data obtained from theunwrapped phase of I-Q data of said signal data; or wherein said signalcomprises a FSK signal and wherein said in-phase sum of reinforceablerepetitive synchronous data is obtained from a differentiation of deltaphase data obtained from the unwrapped phase of I-Q data of said signaldata; or wherein said signal comprises a FMCW signal and wherein saidin-phase sum of reinforceable repetitive synchronous data is obtainedfrom a double differentiation of delta phase data obtained from theunwrapped phase of I-Q data of said signal data.
 18. The method of claim16, wherein said signal comprises a FSK signal and wherein said in-phasesum of reinforceable repetitive synchronous data is obtained from alevel change detection of the envelope obtained from the differentiatedI and Q data of said signal data; or wherein said signal comprises aFMCW signal and wherein said in-phase sum of reinforceable repetitivesynchronous data is obtained from a frequency peak detection of theenvelope obtained from the differentiated I and Q data of said signaldata.
 19. The method of claim 16, wherein said signal comprises a PSKsignal and wherein said in-phase sum of reinforceable repetitivesynchronous data is obtained from the envelope of the unwrapped phase ofI and Q data of said signal data encoding a fixed periodic data pattern;or wherein said signal comprises a FSK signal and wherein said in-phasesum of reinforceable repetitive synchronous data is obtained from theenvelope obtained from the differentiated I and Q data of said signaldata encoding a fixed periodic data pattern; or wherein said signalcomprises a FMCW signal and wherein said in-phase sum of reinforceablerepetitive synchronous data is obtained from the envelope obtained fromthe differentiated I and Q data of said signal data encoding a fixedperiodic data pattern.
 20. The method of claim 10, further comprisingdetermining a minimum element length of said repetitive synchronousevent based on said in-phase sum of reinforceable repetitive synchronousdata obtained from said signal data.
 21. The method of claim 10, furthercomprising receiving said signal data; and performing said step ofdetecting the presence of said repetitive synchronized signal event inreal time as said signal data is received.
 22. A system for processingsignal data, comprising one or more system components configured to:receive said signal data; obtain reinforceable repetitive synchronousdata from said I-Q data; obtain time-time data from said reinforceablerepetitive synchronous data, said time-time data comprising multipledata traces; and add said time-time data in phase across said multipledata traces to obtain a sum of said time-time data.
 23. The system ofclaim 22, wherein said reinforceable repetitive synchronous datacomprises rectified average delta data.
 24. The system of claim 22,wherein said one or more system components are further configured toidentify a high amplitude event in said sum of said time-time data, andto determine the presence of a repetitive synchronous event in saidsignal data from said high amplitude event.
 25. The system of claim 22,wherein said one or more system components are further configured toidentify a pattern in said sum of said time-time data, and to determineone or more characteristics of said signal data.
 26. The system of claim22, wherein said one or more system components are further configuredto: provide an initial estimate of element length of a repetitivesynchronous event in said signal data; then orient said multiple datatraces of said time-time data relative to each other based on saidestimated element length prior to adding said time-time data in phaseacross said multiple data traces; and then determine a revised estimateof said element length based on said sum of said time-time data.
 27. Thesystem of claim 26, wherein said one or more system components arefurther configured to obtain an amplitude spectrum of said reinforceablerepetitive synchronous data; and then to provide said initial estimateof element length of said repetitive synchronous event from saidamplitude spectrum of said reinforceable repetitive synchronous data.28. The system of claim 27, wherein said one or more system componentsare further configured to apply a Fast Fourier Transform to saidreinforceable repetitive synchronous data to obtain said amplitudespectrum; and to add in phase data across said multiple data traces toobtain a Radon sum of said time-time data.
 29. The system of claim 22,wherein said one or more system components are further configured to:receive a dwell of said signal data; obtain reinforceable repetitivesynchronous data from said I-Q data of said dwell of said signal data;obtain time-time data from said reinforceable repetitive synchronousdata corresponding to said dwell of said signal data, said time-timedata comprising multiple data traces; and add said time-time data inphase across said multiple data traces to obtain a sum of said time-timedata corresponding to said dwell of said signal data.
 30. The system ofclaim 22, wherein said one or more system components are furtherconfigured to: receive multiple dwells of said signal data; obtainreinforceable repetitive synchronous data from said I-Q data of each ofsaid multiple dwells of said signal data; add said reinforceablerepetitive synchronous data corresponding to said multiple dwells ofsaid signal data to obtain a sum of reinforceable repetitive synchronousdata; obtain time-time data from said sum of reinforceable repetitivesynchronous data, said time-time data comprising multiple data traces;and add said time-time data from said sum of reinforceable repetitivesynchronous data in phase across said multiple data traces to obtain asum of said time-time data corresponding to said multiple dwells of saidsignal data.
 31. A system for detecting repetitive synchronized signalevents, said system comprising: an event detector, said event detectorbeing configured to receive signal data and obtain an in-phase sum ofreinforceable repetitive synchronous data from said signal data; andwherein said event detector is further configured to detect the presenceof a repetitive synchronized signal event in said signal data from saidin-phase sum of reinforceable repetitive synchronous data obtained fromsaid signal data.
 32. The method of claim 31, wherein said reinforceablerepetitive synchronous data comprises rectified average delta data. 33.The system of claim 31, wherein said signal data comprises raw signaldata that includes at least one signal; wherein said repetitivesynchronized signal event corresponds to said at least one signal; andwherein said system further comprises receive path circuitry coupled toreceive said raw signal data from at least one antenna, said receivepath circuitry being coupled between said antenna and said eventdetector.
 34. The system of claim 33, wherein said signal comprises aphase-shift key (PSK) signal and said repetitive synchronized signalevent comprises a phase transition in said PSK signal; or wherein saidsignal comprises a frequency modulated continuous wave (FMCW) signal andsaid repetitive synchronized signal event comprises a frequency slopechange in said FMCW signal; or wherein said signal comprises afrequency-shift key (FSK) signal and said repetitive synchronized signalevent comprises a frequency transition in said FSK signal.
 35. Thesystem of claim 33, wherein said repetitive synchronized signal eventcomprises an envelope in the said signal encoding a fixed periodic datapattern.
 36. The method of claim 33, wherein said signal comprises aradar signal.
 37. The system of claim 31, wherein said event detector isfurther configure to determine the signal type of said signal based onsaid in-phase sum of reinforceable repetitive synchronous data obtainedfrom said signal data.
 38. The system of claim 37, wherein said signalcomprises a PSK signal and wherein said event detector is configured toobtain an in-phase sum of reinforceable repetitive synchronous data fromdelta phase data obtained from the unwrapped phase of I-Q data of saidsignal data; or wherein said signal comprises a FSK signal and whereinevent detector is configured to obtain said in-phase sum ofreinforceable repetitive synchronous data from a differentiation ofdelta phase data obtained from the unwrapped phase of I-Q data of saidsignal data; or wherein said signal comprises a FMCW signal and whereinsaid event detector is configured to obtain an in-phase sum ofreinforceable repetitive synchronous data from a double differentiationof delta phase data obtained from the unwrapped phase of I-Q data ofsaid signal data.
 39. The system of claim 37, wherein said signalcomprises a FSK signal and wherein said event detector is configured toobtain an in-phase sum of reinforceable repetitive synchronous data froma level change detection of the envelope obtained from thedifferentiated I and Q data of said signal data; or wherein said signalcomprises a FMCW signal and wherein said event detector is configured toobtain an in-phase sum of reinforceable repetitive synchronous data froma frequency peak detection of the envelope obtained from thedifferentiated I and Q data of said signal data.
 40. The system of claim37, wherein said signal comprises a PSK signal and wherein said eventdetector is configured to obtain an in-phase sum of reinforceablerepetitive synchronous data from the envelope of the unwrapped phase ofI and Q data of said signal data encoding a fixed periodic data pattern;or wherein said signal comprises a FSK signal and wherein said eventdetector is configured to obtain an in-phase sum of reinforceablerepetitive synchronous data from the envelope obtained from thedifferentiated I and Q data of said signal data encoding a fixedperiodic data pattern; or wherein said signal comprises a FMCW signaland wherein said event detector is configured to obtain an in-phase sumof reinforceable repetitive synchronous data from the envelope obtainedfrom the differentiated I and Q data of said signal data encoding afixed periodic data pattern.
 41. The system of claim 31, wherein saidevent detector is further configured to determine a minimum elementlength of said repetitive synchronous event based on said in-phase sumof reinforceable repetitive synchronous data obtained from said signaldata.
 42. The system of claim 31, wherein said event detector is furtherconfigured to receive said signal data and to detect the presence ofsaid repetitive synchronized signal event in real time as said signaldata is received.